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Method for identifying surface electromyography (sEMG) on basis of empirical mode decomposition (EMD) sample entropy

A technology of myoelectric signal and recognition method, applied in the field of pattern recognition, which can solve the problems of real-time control of myoelectric prosthetic hand and weak anti-noise ability

Active Publication Date: 2013-03-13
HANGZHOU DIANZI UNIV
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Problems solved by technology

These nonlinear algorithms have solved the feature extraction problem of myoelectric signal very well. However, these feature extraction methods require long-term stable sEMG signals, and their anti-noise ability is weak, so they cannot control the myoelectric prosthetic hand well in real time.

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  • Method for identifying surface electromyography (sEMG) on basis of empirical mode decomposition (EMD) sample entropy
  • Method for identifying surface electromyography (sEMG) on basis of empirical mode decomposition (EMD) sample entropy
  • Method for identifying surface electromyography (sEMG) on basis of empirical mode decomposition (EMD) sample entropy

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Embodiment Construction

[0056] The embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings: this embodiment is implemented on the premise of the technical solution of the present invention, and provides detailed implementation methods and specific operating procedures.

[0057] Such as figure 1 As shown, this embodiment includes the following steps:

[0058] The first step is to obtain the sample data of the human upper limb EMG signal, specifically: firstly, the human upper limb EMG signal is picked up by the EMG signal acquisition instrument, and then the energy threshold is used to determine the action signal of the EMG signal.

[0059] (1) Collect the EMG signals of the upper limbs of the human body. The subjects performed 80 groups of 4 kinds of forearm movements, wrist up, wrist down, fist stretching and fist clenching, with a total of 320 sets of data. The extensor carpi ulnaris and flexor carpi ulnaris of the upper limbs were selec...

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Abstract

The invention provides a method for identifying surface electromyography (sEMG) on the basis of empirical mode decomposition (EMD) sample entropy. The method comprises the following steps: acquiring the corresponding sEMG from the related muscle tissue; performing EMD by using an actuating signal of the sEMG determined by energy threshold; adaptively selecting a plurality of intrinsic mode function (IMF) components comprising electromyographic signal effective information according to a frequency availability method; superposing the IMF components to serve as effective electromyographic signals and evaluating the sample entropy; and inputting the sample entropy serving as feature vector into a clustering classifier based on a spindle kernel clustering algorithm to realize identification on an upper limb multi-locomotion mode of the electromyographic signal. The sample entropy can disclose the complexity of the sEMG from a short time sequence, represents the tiny change condition of the electromyographic signal well, has high antijamming capability, simple algorithm and high calculation speed, and is particularly suitable for real-time processing of the electromyographic signal.

Description

technical field [0001] The invention belongs to the field of pattern recognition, and relates to a method for pattern recognition of myoelectric signals, in particular to a method for recognizing patterns of upper limb multi-motion based on myoelectric signals, which is applied to a myoelectric prosthetic hand. Background technique [0002] Surface electromyography (sEMG) is a bioelectrical signal related to neuromuscular activity recorded from the surface of human skeletal muscle through surface electromyographic pickup electrodes, which contains a lot of information related to limb movement. Limb movements have different muscle contraction patterns, and the characteristics of EMG signals will also be different. Through the analysis of these characteristics, different movement patterns of the limbs can be distinguished. Therefore, it is not only widely used in clinical diagnosis, sports medicine and other fields, It has also become an ideal control signal for prosthetic con...

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Application Information

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IPC IPC(8): A61F2/72A61B5/0488
Inventor 席旭刚朱海港罗志增张启忠佘青山
Owner HANGZHOU DIANZI UNIV
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